def _crop(image, boxes, labels):
height, width, _ = image.shape
if len(boxes)== 0:
return image, boxes, labels
while True:
mode = random.choice((
None,
(0.1, None),
(0.3, None),
(0.5, None),
(0.7, None),
(0.9, None),
(None, None),
))
if mode is None:
return image, boxes, labels
min_iou, max_iou = mode
if min_iou is None:
min_iou = float('-inf')
if max_iou is None:
max_iou = float('inf')
for _ in range(50):
scale = random.uniform(0.3,1.)
min_ratio = max(0.5, scale*scale)
max_ratio = min(2, 1. / scale / scale)
ratio = math.sqrt(random.uniform(min_ratio, max_ratio))
w = int(scale * ratio * width)
h = int((scale / ratio) * height)
l = random.randrange(width - w)
t = random.randrange(height - h)
roi = np.array((l, t, l + w, t + h))
iou = matrix_iou(boxes, roi[np.newaxis])
if not (min_iou <= iou.min() and iou.max() <= max_iou):
continue
image_t = image[roi[1]:roi[3], roi[0]:roi[2]]
centers = (boxes[:, :2] + boxes[:, 2:]) / 2
mask = np.logical_and(roi[:2] < centers, centers < roi[2:]) \
.all(axis=1)
boxes_t = boxes[mask].copy()
labels_t = labels[mask].copy()
if len(boxes_t) == 0:
continue
boxes_t[:, :2] = np.maximum(boxes_t[:, :2], roi[:2])
boxes_t[:, :2] -= roi[:2]
boxes_t[:, 2:] = np.minimum(boxes_t[:, 2:], roi[2:])
boxes_t[:, 2:] -= roi[:2]
return image_t, boxes_t,labels_t
def _crop(image, boxes, labels):
height, width, _ = image.shape
if len(boxes)== 0:
return image, boxes, labels
while True:
mode = random.choice((
None,
(0.1, None),
(0.3, None),
(0.5, None),
(0.7, None),
(0.9, None),
(None, None),
))
if mode is None:
return image, boxes, labels
min_iou, max_iou = mode
if min_iou is None:
min_iou = float('-inf')
if max_iou is None:
max_iou = float('inf')
for _ in range(50):
scale = random.uniform(0.3,1.)
min_ratio = max(0.5, scale*scale)
max_ratio = min(2, 1. / scale / scale)
ratio = math.sqrt(random.uniform(min_ratio, max_ratio))
w = int(scale * ratio * width)
h = int((scale / ratio) * height)
l = random.randrange(width - w)
t = random.randrange(height - h)
roi = np.array((l, t, l + w, t + h))
iou = matrix_iou(boxes, roi[np.newaxis])
if not (min_iou <= iou.min() and iou.max() <= max_iou):
continue
image_t = image[roi[1]:roi[3], roi[0]:roi[2]]
centers = (boxes[:, :2] + boxes[:, 2:]) / 2
mask = np.logical_and(roi[:2] < centers, centers < roi[2:]) \
.all(axis=1)
boxes_t = boxes[mask].copy()
labels_t = labels[mask].copy()
if len(boxes_t) == 0:
continue
boxes_t[:, :2] = np.maximum(boxes_t[:, :2], roi[:2])
boxes_t[:, :2] -= roi[:2]
boxes_t[:, 2:] = np.minimum(boxes_t[:, 2:], roi[2:])
boxes_t[:, 2:] -= roi[:2]
return image_t, boxes_t,labels_t
def __call__(self, image, boxes=None, labels=None):
height, width, _ = image.shape
while True:
# randomly choose a mode
mode = random.choice(self.sample_options)
if mode is None:
return image, boxes, labels
min_iou, max_iou = mode
if min_iou is None:
min_iou = float('-inf')
if max_iou is None:
max_iou = float('inf')
# max trails (50)
for _ in range(50):
current_image = image
w = random.uniform(0.3 * width, width)
h = random.uniform(0.3 * height, height)
# aspect ratio constraint b/t .5 & 2
if h / w < 0.5 or h / w > 2:
continue
left = random.uniform(width - w)
top = random.uniform(height - h)
# convert to integer rect x1,y1,x2,y2
rect = np.array(
[int(left),
int(top),
int(left + w),
int(top + h)])
# calculate IoU (jaccard overlap) b/t the cropped and gt boxes
# overlap = jaccard_numpy(boxes, rect)
# # is min and max overlap constraint satisfied? if not try again
# if overlap.min() < min_iou and max_iou < overlap.max():
# continue
iou = matrix_iou(boxes, rect[np.newaxis])
if len(iou) == 0:
continue
if not (min_iou <= iou.min() and iou.max() <= max_iou):
continue
# cut the crop from the image
current_image = current_image[rect[1]:rect[3],
rect[0]:rect[2], :]
# keep overlap with gt box IF center in sampled patch
centers = (boxes[:, :2] + boxes[:, 2:]) / 2.0
# mask in all gt boxes that above and to the left of centers
m1 = (rect[0] < centers[:, 0]) * (rect[1] < centers[:, 1])
# mask in all gt boxes that under and to the right of centers
m2 = (rect[2] > centers[:, 0]) * (rect[3] > centers[:, 1])
# mask in that both m1 and m2 are true
mask = m1 * m2
# have any valid boxes? try again if not
if not mask.any():
continue
# take only matching gt boxes
current_boxes = boxes[mask, :].copy()
# take only matching gt labels
current_labels = labels[mask]
# should we use the box left and top corner or the crop's
current_boxes[:, :2] = np.maximum(current_boxes[:, :2],
rect[:2])
# adjust to crop (by substracting crop's left,top)
current_boxes[:, :2] -= rect[:2]
current_boxes[:, 2:] = np.minimum(current_boxes[:, 2:],
rect[2:])
# adjust to crop (by substracting crop's left,top)
current_boxes[:, 2:] -= rect[:2]
return current_image, current_boxes, current_labels
